javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829785324
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File path: flink-python/pyflink/datastream/data_stream.py
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@@ -1168,6 +1168,96 @@ def process_element(self, value, ctx:
'KeyedProcessFunction.Context'):
return self.process(FilterKeyedProcessFunctionAdapter(func),
self._original_data_type_info)\
.name("Filter")
+ def sum(self, position_to_sum: Union[int, str]) -> 'DataStream':
+ """
+ Applies an aggregation that gives a rolling sum of the data stream at
the
+ given position grouped by the given key. An independent aggregate is
kept
+ per key.
+
+ Example(Tuple data to sum):
+ ::
+
+ >>> ds = env.from_collection([('a', 1), ('a', 2), ('b', 1), ('b',
5)])
+ >>> ds.key_by(lambda x: x[0]).sum(1)
+
+ Example(Row data to sum):
+ ::
+
+ >>> ds = self.env.from_collection([('a', 1), ('a', 2), ('a', 3),
('b', 1), ('b', 2)],
+ ...
type_info=Types.ROW([Types.STRING(), Types.INT()]))
+ >>> ds.key_by(lambda x: x[0]).sum(1)
+
+ Example(Row data with fields name to sum):
+ ::
+
+ >>> ds = self.env.from_collection(
+ ... [('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2)],
+ ... type_info=Types.ROW_NAMED(["key", "value"],
[Types.STRING(), Types.INT()])
+ ... )
+ >>> ds.key_by(lambda x: x[0]).sum("value")
+
+ :param position_to_sum:
+ The field position in the data points to sum, type can be int or
str.
+ This is applicable to Tuple types, and {pyflink.common.types.Row}
types.
+ :return: The transformed DataStream.
+ """
+ if not isinstance(position_to_sum, int) and not
isinstance(position_to_sum, str):
+ raise TypeError("The input must be a int or str type for locate
the value to sum")
+
+ output_type =
_from_java_type(self._original_data_type_info.get_java_type_info())
+
+ class SumKeyedProcessFunctionAdapter(KeyedProcessFunction):
Review comment:
Its a good idea
By logic, Apply reduce as the underlying implementation of sum operation is
good design i think.
I'll try to apply ReduceFunction from the new design to see if it's easier
to implement.
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